1. Technical Field
This invention relates to correlation processors, and more particularly to a connectionist architecture for correlating entities in a first set of multi-dimensional data with entities in a second set of multi-dimensional data.
2. Discussion
A wide variety of data processing tasks involve solving correlation problems. Generally, correlation problems involve the correlation, or assignment, of elements in one set of data to elements in another set of data. Correlation problems are present in applications including radar/sonar/IR target identification, machine inspection, medical, or financial pattern recognition tasks.
Correlation problems are often difficult to solve because there may not be a single solution, but instead there may be a range of solutions over which the best solution is sought. Also, these problems frequently involve a combinatorial "explosion", or exponential blow-up in the number of possible answers. Many of the current approaches to solving these problems have a number of drawbacks. One difficulty is in developing algorithms and software, which can take a great deal of time and resources. In addition, software approaches are extremely CPU intensive for problems involving large numbers of data elements and high dimensionality data. Conventional approaches are simply not feasible for real-time or near real-time solutions.
An alternative approach involves the use of connectionist or neural network architectures. Connectionist architectures generally refers to systems which involve massively connected, fine-grained processing elements. Neural networks are connectionist architectures which are so named because of their similarity to biological networks in their highly interconnected structure and in their ability to adapt to data and exhibit self-learning. These approaches have the advantage of operating without requiring the development of an explicit algorithm and software. For example, see U.S. Pat. No. 4,660,166, issued to J. Hopfield, where a type of neural network is used to solve association problems, such as the traveling salesman problem. Another related technique is known as simulated annealing. See S. Kirkpatrick, Gelatt, and Vecchi: "Optimization By Simulated Annealing" 220 SCIENCE, pages 671-680 (1983).
Unfortunately, while slow, software simulations of connectionist architectures have been developed, practical high-density hardware embodiments taking fully advantage of the inherent parallelism of these systems have not yet been perfected. Also, neural networks, such as the one described in U.S. Pat. No. 4,660,166 are generally not fast enough for real-time application for high-dimensionality data association for a large numbers of data elements. For example, the system in that patent is not able to solve the traveling salesman problem for significantly more than 30 cities.
Thus, it would be desired to provide an information processor which can provide real-time solutions to multi-dimensionality correlation problems involving a large number of data elements. Further, it would be desirable to provide such an information processor which requires minimal algorithm development, minimal software development, and minimal preprocessing. Further, it would be desirable to provide such an information processor which can be implemented using current hardware technology at a reasonable cost.